论文标题
自动检测和识别图案化物种中的个体
Automatic Detection and Recognition of Individuals in Patterned Species
论文作者
论文摘要
Visual Animal Biometrics正在迅速获得流行，因为它可以为野生动植物监测应用一种非侵入性和成本效益的方法。相机陷阱的广泛使用导致了大量收集的图像，因此难以管理视觉内容的手动处理。在这项工作中，我们开发了一个框架，用于自动检测和识别虎，斑马和美洲虎等不同图案物种中的个体。大多数现有系统主要依靠手动输入来定位动物，该动物不能很好地扩展到大型数据集。为了使检测过程自动化，同时保持对模糊，部分阻塞，照明和姿势变化的鲁棒性，我们使用最近提出的更快的RCNN对象检测框架来有效地检测图像中的动物。我们进一步从动物侧面的Alexnet提取特征，并训练逻辑回归（或线性SVM）分类器以识别个体。我们主要在相机陷阱老虎图像数据集上测试和评估我们的框架，该数据集包含整体图像质量，动物姿势，比例和照明的图像。我们还评估了我们在斑马和捷豹图像上的识别系统，以显示对其他图案化物种的概括。与最先进的识别技术相比，我们的框架在摄像头捕获的老虎图像和相似或更好的个人识别性能中提供了完美的检测结果。
Visual animal biometrics is rapidly gaining popularity as it enables a non-invasive and cost-effective approach for wildlife monitoring applications. Widespread usage of camera traps has led to large volumes of collected images, making manual processing of visual content hard to manage. In this work, we develop a framework for automatic detection and recognition of individuals in different patterned species like tigers, zebras and jaguars. Most existing systems primarily rely on manual input for localizing the animal, which does not scale well to large datasets. In order to automate the detection process while retaining robustness to blur, partial occlusion, illumination and pose variations, we use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images. We further extract features from AlexNet of the animal's flank and train a logistic regression (or Linear SVM) classifier to recognize the individuals. We primarily test and evaluate our framework on a camera trap tiger image dataset that contains images that vary in overall image quality, animal pose, scale and lighting. We also evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species. Our framework gives perfect detection results in camera trapped tiger images and a similar or better individual recognition performance when compared with state-of-the-art recognition techniques.
